class_docstr: Computes Binary Cross Entropy (BCE) loss. The BCELoss class expects output from Sigmoid function.
docstr: Input types definitions for AnguarLoss.
          
docstr_tok: ["Input","types","definitions","for","AnguarLoss","."]
code: def input_types(self):
      """Input types definitions for AnguarLoss."""
      return {
          "probs": NeuralType(("B", "T", "C"), ProbsType()),
          "labels": NeuralType(("B", "T", "C"), LabelsType()),
          "signal_lengths": NeuralType(tuple("B"), LengthsType()),
      }
  
file_path: NeMo/nemo/collections/asr/losses/bce_loss.py
imports: import torch
  from nemo.core.classes import Loss, Typing, typecheck
  from nemo.core.neural_types import LabelsType, LengthsType, LossType, NeuralType, ProbsType
label_desc: Use image node_id 1 for calling the BCELoss obj's underlying member method code with example usage: obj.input_types() and returns: dict